2-16-10_MHT_Uncooking_the_books_from_toxic_paper_sub-prime_morgages_= cds_cdos_material_misstatements_financial_services_industry_bank.htm

Int. J. Economics and Accounting, Vol. X, No. Y, xxxx 1

Uncooking the books from toxic paper sub-prime mortgages CDS = and CSOs material misstatements of the financial services industry: = crisis challenges & counter party surveillance of collateralised debt obligations & off-shore tax havens =

New References:

Rushinek, A. and Rushinek, S. “Offshore Internet Web Server, e-Commerce, and Taxation: Reducing Legal Liabilities”, International Tax planning, Anti-Money Laundering and Compliance Conference - Miami Beach, Florida, May, 2002.

Rushinek, A. and Rushinek, S. “Computer Security and Accounting Information Systems: Updates and Challenges”, Internati= onal Tax planning, Anti-Money Laundering and Compliance Conference - Miami Beach, Florida, May, 2002.

Rushinek, A. and Rushinek, S. “Forensic Accounting and Computer Forensics: Implications for Offshore Companies”, Internati= onal Tax planning, Anti-Money Laundering and Compliance Conference - Miami Beach, Florida, May, 2002.

Rushinek, A. and Rushinek, S. “DVD Technology: Academic & Professional Productivity Tutorial & Workshop” ICPQR (International Conference on Productivity and Quality Research) 2004 Conference, Feb. 19, = 2004 1 Day Workshop, Miami, Florida.

Rushinek, A. and Rushinek, S. ̶= 0;Sarbanes Oxley Microsoft Accelerator Forensic Accounting, Expert Witness Testimony, & Computer Litigation Support Workshop”, American Accounting Association Annual Meeting, Saturday, August 4,2005, San Francisco, California (http://aaahq.org/AM2005/cpe/cpe02.htm), sponsored by Teaching and Curricul= um Section.

&nb= sp;

Rushinek= , A. and Rushinek, S. “Assessment Automation Software for Accounting, Teaching, and Recruiting Programs”, American Accounting Associat= ion Meeting, August 6, 2006, Washington, D.C.,= http://aaahq.org/A= M2006/cpe/cpe01.htm, sponsored by Teaching and Curriculum Section.

&nb= sp;

Rushinek, A. and Rushinek, S. “Audited Google Video Professors' e-Store Pod-Casts: How to Teach, Service & Research using WWW Assessment DVD of Microsoft® Office Accounting Technologies for the Wireless Classroom”, American Accounting Association Annual Meeting, August 4, = 2007, Chicago, Illinois http://aaahq.org/am2007/cpe/cpe03.htm, sponsored by Teaching and Curricul= um Section.

Rushinek, A. and Rushinek, S. “Assessment CPE Video Podcasts and Blogs "Hands-on:" Consult, Publish & T= each Forensic Auditing, Expert Witness Testimony & Microsoft® Office Accounting Computer Litigation Support, Saturday, August 4, 2007, Chicago, Illinois, http://aaahq.org/am2007/cpe/cpe08.htm, sponsored by Teaching and Curricul= um Section.

Rushinek, A. = and Rushinek, S. “The Role of the Forensic Accountant in Calculating the Damages Using the ‘But-if Analysis in a Case of Internet Day Trader &= amp; Online Broker Misconduct Litigation”, JOURNAL OF FORENSIC ACCOUNTING, Vol. 1, No. 2, 241-250, 2001. http://www.rte= dwards.com/journals/JFA/contents.html

his exit plan= and cover his short position, before the stock market trend reversed itself.

Rushinek, A. and Rushinek, S. “E-Commerce Security Measures: Are They Worth It?”, ACM: UBIQUITY, Volume 3, issue 39, November 12, 2002 pp. 1-12.<= o:p>

Rushinek, A. and Rushinek, S. “Short Sel= ling Opportunity Cost Of Stock Market Losses Damages Software: Internet Mathemat= ical Power, Log, Polynomial, Exponential & Insider Trading Programs”, JOURNAL OF FORENSIC ACCOUNTING, (Accepted for Publication).? http://www.rte= dwards.com/journals/JFA/contents.html

Rushinek, A. and Rushinek, S. "Forensic Audits of Stock Market Forecasts For Tex= aco & The International Oil and Gas Industry & Financial Models for Onl= ine Broker Litigation", OIL, GAS, & ENERGY QUARTERLY, Vol. 51, No. 4, June, 2003, 659-678.

Rushinek, A. and Rushinek, S. “Automated Internet Trading Forecast Variance Analysis: An Oil and Gas Industry And BP Corporation Case Study”, OIL= , GAS, & ENERGY QUARTERLY, Volume 52 Number 1 September 2003, 35-54.

Rushinek, A. and Rushinek S. “What Makes a Publisher Important? Maximization of Internet Citations Methodology”, ACM: UBIQUITY, Volume 4, Issue 12, <= span class=3DGramE>May 13 - 19, 2003.

Rushinek, A. and Rushinek S. “The University of DVD”, ACM: UBIQUITY, Volu= me 4, Issue 41, May 13 - 19, 2003.

Rushinek, A. and Rushinek, S. "Patents & Provisional Business Method Patent Application (PPA) Disclosure Document Program (DDP) For Building & Test= ing Quality Research Productivity Of Expert Witness Testimony & Computer Litigation Support Of Trade Secrets & IP”, PRODUCTIVITY AND QUALITY MANAGEMENT FRONTIERS, Vol X (2004).

Rushinek, A. and Rushinek S. “What Makes Users Unhappy: Share-Point Team Services Web Server Security”, ACM: UBIQUITY, Volume 6, Issue 2, February 2005. retrieved 10-31-2009 http://www.acm.org/ubiquity/views/v6i2_= rushinek.html

Rushinek, A. and Rushinek S. “Internet W= eb Server Comparative Forecasting Sales Models: Reversed Accounting of Texaco = Corp and The Oil & Gas Industry linear, Exponential, Power, Polynomial & Logarithmic Univariate Regressions”, OIL, GAS, & ENERGY QUARTERLY, Volume 54, No. 2, November 2005, 87-95

Mark Friedman, Avi Rushinek, Sara Rushinek “The Effect on Achievement of Using Emerging Technology= in the Managerial Accounting Course”, THE ACCOUNTING EDUCATORS'JOURNAL,Volume 16 (2006)

Retrieved 11-1-98: = http://docs.google.com/gview?a=3Dv&q=3Dcache:kPTYgWPEA7kJ:www.aejournal= .com/ojs/index.php/aej/article/view/32/32+THE+ACCOUNTING+EDUCATORS'+JOURNAL= ,+Volume+16+(2006)&hl=3Den&gl=3Dus&pid=3Dbl&srcid=3DADGEESi= Pfkg-LaynTualjyzZnUzb8R4LY7zRfEU6I00eH7maeYwrmbAxGTM2CEQ0MRmAlS

= http://www.aejournal.com/ojs/index.php/aej/article/viewFile/32/32<= /o:p>

Rushinek, A. and Rush= inek S. “An Automated Business Intelligent Stock Market Matching Engine For the Oil and Gas Industry: Materiality of Violating the Normality Assumption= of Linear Regression Control Analysis”, OIL, GAS, & ENERGY QUARTERLY, Volume 56, No. 2, November 2007, 435-448.

Retrieved 11-1-98: = http:= //www.acquirecontent.com/titles/oilgas-energy-quarterly

= Rushinek, A. and Rushinek S. “Optimal Forecasting Models For The Oil & Gas Industry: A Study Of The Daily Dow Jones U.S. Oil & Gas Index And ConocoPhillips With The Aid Of The Virtual Internet Office= ”, OIL, GAS, & ENERGY QUARTERLY, Volume 57, No. 1, June, 2008, 893-901.

= http://www.docstoc.com/docs/3866699/OFFSHORE-= BANKING-TAX-PLANNING--THE-VIRTUAL-INTERNET-OFFICE-OFFSHORE BANKING, TAX-PLANNING, = THE VIRTUAL INTERNET OFFICE ...

2 Rushinek, A. a= nd Rushinek, S., “Oil and Gas Market Sector & Company Relative <= b>... Theory Applied to Shell Oil”, Oil, Gas, & Tax Quarterly, March 1997, Vol. ...
www.docstoc.com/.../OFFSHORE-BANKING-TAX-PLANNING--THE-VIRTUAL-IN= TERNET-OFFICE-

<= /o:p>

\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\= \\\\\\\

Appendix A;

Sample AAA CPE Workshops & Presentations

Rus= hinek, A. and Rushinek, S. “Offshore Internet Web Server, e-Commerce, and Taxation: Reducing Legal Liabilities”, In= ternational Tax planning, Anti-Money Laundering and Compliance Conference - Miami Beach, Florida, May, 2002.

&nb= sp;

Rus= hinek, A. and Rushinek, S. “Computer Security and Accounting Information Systems: Updates and Challenges”, Interna= tional Tax planning, Anti-Money Laundering and Compliance Conference - Miami Beach, Florida, May, 2002.

&nb= sp;

Rus= hinek, A. and Rushinek, S. “Forensic Accounting and Computer Forensics: Implications for Offshore Companies”, Int= ernational Tax planning, Anti-Money Laundering and Compliance Conference - Miami Beach, Florida, May, 2002.

Rushinek, A. and Rushinek, S. “DVD Technology: Academic & Professional Productivity Tutorial & Workshop” ICP= QR 2004 Conference, Feb. 19, 2004 1 Day Workshop, Miami, Florida.

Rushinek, A. and Rushinek, S. “Sarbanes Oxley Microsoft Acceler= ator Forensic Accounting, Expert Witness Testimony, & Computer Litigation Support Workshop”, American Accounting Association Annual Meeting 200= 5, Saturday, August 4,2005, San Francisco, California, sponsored by Teaching a= nd Curriculum Section.

http://aaahq.org/AM= 2005/cpe/cpe02.htm

An International Meeting of

the American Accounting Association

2005 Annual Meeting

August 7–10, 2005

San Francisco, California

Come to the City by the Bay!

---------------------------------------------------------------------= -----------

CPE Session 2: Saturday, August 6, 8:00 AM – 4:00 PM

Sarbanes-Oxley Microsoft® Accelerator Forensic Accounting,=

Expert Witness Testimony, and Computer Litigation Support

Description/Objectives:

Sarbanes-Oxley Microsoft Accelerator Forensic accounting workshop demonstrates how Accounting academics and practitioners can use Microsoft S= hare Point Team Services and Share Point Server to assist an internal audit team= in implementing, investigating, and improving compliance audit services. It describes how to become an Accounting Expert Witness and how to provide Exp= ert Witness Testimony, with an emphasis on HIPAA (Health Insurance Portability = and Accountability), Sarbanes-Oxley, and Basel II Compliance Forensic Accountin= g.

We will discuss R&D for experienced expert witnesses, including t= he issues of dealing with the forensic team of professionals. This workshop focuses on using forensic accounting techniques in general, and providing Computer Litigation Support in particular. The workshop objectives include helping experienced expert witnesses to review their skills and upgrade the= m, while demonstrating to prospective expert witnesses the nature of the work,= its intellectual challenges and financial opportunities and rewards. We plan to meet such objectives, by lectures, role playing, demonstrations, and most importantly active participation of the audience as well as discussions and arguments of pros and cons of various issues.

Format/Structure:

DVDs for litigation support, including the pros and cons of different standards such as DVD+/-RW/VR (Digital Versatile Disk, Read Write, and Video Recording), DVD-RAM (Random Access Memory), HD (High Definition)-DVD, Blu-R= ay DVD, DVD Copying, dubbing, burning, and recording, focusing on the technical and legal facets.=

Intended Audience:

This study targets practicing and academic litigation and litigation support team members, including lawyers, economists, accountants, auditors, doctors, engineers, corporate executives, expert witnesses, plaintiffs and defendants, as well as students and others interested in the topic. This workshop will review the financial opportunities for expert witnesses, fee structures, relationship with the legal profession, opp= ortunities for students that want to specialize in the area of forensic accounting and litigation support, and/or combine the study of accounting and law, and computer information systems.

Presenters:=

Sara Rushinek, University of Miami

Avi Rushinek, University of Miami

Sponsor:

Teaching and Curriculum Section

---------------------------------------------------------------------= -----------

* Denotes special requirements or prerequisite

Back to CPE Listings Back to Annual Meeting Home Page

=	An International Meeting of the American Accounting Association 2005 Annual Meeting Augu= st 7–10, 2005 San Francisco, California Come to the City by the Bay!

CPE Session 2: Saturday, August 6, 8:00 AM – 4:00 PM

Sarb= anes-Oxley Microsoft® Accelerator Forensic Accounting,
Expert Witness Testimony, and Computer Litigation Support

Description/Objectives:=
Sarbanes-Oxley Microsoft Accelerator Forensic accounting workshop demonstrates how Accounting academics and practitioners can use Microso= ft Share Point Team Services and Share Point Server to assist an internal audit team in implementing, investigating, and improving compliance aud= it services. It describes how to become an Accounting Expert Witness and h= ow to provide Expert Witness Testimony, with an emphasis on HIPAA (Health Insurance Portability and Accountability), Sarbanes-Oxley, and Basel II Compliance Forensic Accounting.

We wi= ll discuss R&D for experienced expert witnesses, including the issues = of dealing with the forensic team of professionals. This workshop focuses = on using forensic accounting techniques in general, and providing Computer Litigation Support in particular. The workshop objectives include helpi= ng experienced expert witnesses to review their skills and upgrade them, w= hile demonstrating to prospective expert witnesses the nature of the work, i= ts intellectual challenges and financial opportunities and rewards. We pla= n to meet such objectives, by lectures, role playing, demonstrations, and mo= st importantly active participation of the audience as well as discussions= and arguments of pros and cons of various issues.

Format/Structure:
DVDs for litigation support, including the pros and cons of different standards such as DVD+/-RW/VR (Digital Versatile Disk, Read Write, and Video Recording), DVD-RAM (Random Access Memory), HD (High Definition)-= DVD, Blu-Ray DVD, DVD Copying, dubbing, burning, and recording, focusing on = the technical and legal facets.

Intended Audience:
This study targets practicing and academic litigation and litigation support team members, including lawyers, economists, accountants, audit= ors, doctors, engineers, corporate executives, expert witnesses, plaintiffs = and defendants, as well as students and others interested in the topic. This workshop will review the financial opportunities for expert witnesses, = fee structures, relationship with the legal profession, opportunities for students that want to specialize in the area of forensic accounting= and litigation support, and/or combine the study of accounting and law, and computer information systems.

Presenters:
Sara Rushinek, University of Miami
Avi Rushinek, University of Miami

Sponsor:
Teaching and Curriculum Section

* Denotes special requirements or prerequisite

Back to CPE Listings

B= ack to Annual Meeting Home Page

Rushinek, A. and Rushinek, S. “Assessment Automat= ion Software for Accounting, Teaching, and Recruiting Programs”, American Accounting Association Meeting 2006,August 6, 2006, Washingt= on, D.C., sponsored by Teaching and Curriculum Section. http://aaahq.org/AM2006/cpe/= cpe01.htm http://aaahq.org/pubs/AE= N/2006/AM2006.pdf

Rushinek, A. and Rushinek, S. “Audited Google Video Professors' e-Store Pod-Casts: How to Teach, Service & Research using WWW Assessment DVD of Microsoft® Office Accounting Technologies for the Wireless Classroom,” American Accounting Association Annual Meeting 2007, August 4, 2007, Chicago, Illinois, sponsore= d by Teaching and Curriculum Section. http://aaahq.org/am2007/cpe/cpe03.htm

CPE Session 3: Saturday, August 4, 8:00 AM – 11:00 AM

Audited Google Video Professors' e-Store Pod-Casts: How to Teach, Ser= vice & Research using WWW Assessment DVD of Microsoft® Office Accounting Technologies for the Wireless Classroom

(Accounting)

Description/Objectives:

This workshop focuses on Audited Google Video Professors' e-Stores Podcasts. It describes how university professors record lectures to Podcast them, and distributes them offline and online. It deals with how to stream = free trailers, upload and download clips, Page-View Counts, Buy High Quality, Day-Pass, or View the Full Length of a Clip. It evaluates methods of "How-to" Teach, Service, and Research the WWW (World Wide Web) Assessment DVD, applied not only to supplement instructions and testing, e-learning and CPE distance learning, but also to improve teaching effectiveness, fund other activities, and facilitate business continuity ba= ckup and recovery. Thus, you can back up your computers and lectures on to a DVD, and distribute it from a remote WWW server. We use the new Free Microsoft&r= eg; Office Accounting Technologies as an instructional tool and an example of h= ow to teach, service, and research using new Wireless Bluetooth, Wi-Fi, Blu-ra= y technologies.

Presenters:

Avi Rushinek, Univ= ersity of Miami

Sara Rushinek, Uni= versity of Miami

Sponsor: Teaching = and Curriculum Section.

------------------= --------------------------------------------------------------

Note: CPE fields o= f study are in parentheses

CPE Session 3: Saturday, August 4, 8:00 AM – 11:00 AM

Audited Google Video Professors' e-Store Pod-Casts: How to Teach, Servi= ce & Research using WWW Assessment DVD of Microsoft® Office Accounting Technologies for the Wireless Classroom

(Accounting)

Desc= ription/Objectives:
This workshop focuses on Audited Google Video Professors' e-Stores Podcasts= . It describes how university professors record lectures to Podcast them, and distributes them offline and online. It deals with how to stream free trail= ers, upload and download clips, Page-View Counts, Buy High Quality, Day-Pass, or View the Full Length of a Clip. It evaluates methods of "How-to" = Teach, Service, and Research the WWW (World Wide Web) Assessment DVD, applied not = only to supplement instructions and testing, e-learning and CPE distance learnin= g, but also to improve teaching effectiveness, fund other activities, and facilitate business continuity backup and recovery. Thus, you can back up y= our computers and lectures on to a DVD, and distribute it from a remote WWW ser= ver. We use the new Free Microsoft® Office Accounting Technologies as an instructional tool and an example of how to teach, service, and research us= ing new Wireless Bluetooth, Wi-Fi, Blu-ray technologies.

Pres= enters:
Avi Rushinek, University of Miami
Sara Rushinek, University of Miami

Spon= sor: Teaching and Curriculum Section.

Note= : CPE fields of study are in parentheses

Rushinek, A. and R= ushinek, S. “Assessment CPE Video Podcasts and Blogs "Hands-on:&quo= t; Consult, Publish & Teach Forensic Auditing, Expert Witness Testimony &a= mp; Microsoft® Office Accounting Computer Litigation Support,” Americ= an Accounting Association Annual Meeting 2007, Saturday, August 4, 2007, Chica= go, Illinois, sponsored by Teaching and Curriculum Section. http://aaahq.org/am2007/cpe/= cpe08.htm

CPE Session 8: Saturday, August 4, 1:00 PM – 4:00 PM

Assessment CPE Video Podcasts and Blogs "Hands-on:" Consult, Publish & Teach Forensic Auditing, Expert Witness Testimony & Microsoft® Office Accounting Computer Litigation Support

(Specialized Knowledge & Applications)

Description/Objectives:

Repurpose DVD-recorded lectures using new Blu-ray® and HD-DVD® = in the classroom and CPE, for the CPA, CMA, CIA, CISA/M WWW certificate Preps. Demonstrate download of the Microsoft Office Accounting, and upload the lec= ture on ITunesU®, Google Video®, and YouTube= ®. Review test automation compliance with NASBA, AACSB, COSO, Sarbanes-Oxley, HIPAA, and Basel II. Publish for students, alumnus, professionals, and the general public. Web cast using multi-media streaming tools to be more effec= tive and efficient, and provide a more enjoyable experience. Use the new Microsoft® Office 2007 Accounting, and Vista® involving Effective Learning Strategies, Emerging and Innovative Research, and Internet Research Forums. Instructors encourage existing and prospective faculty and students= to be involved and contribute in Assessing Submissions, Publications, Faculty Development, Teaching, Research, Practice, Service, Awards, and Placement streamline automation and SEO (Search Engine Optimization).

Presenters:

Avi Rushinek, University of Miami

Sara Rushinek, University of Miami

Sponsor:

Teaching and Curriculum Section.

-----------------------------------------------------------------------= ---------

Note: CPE fields of study are in parentheses

CPE Session 8: Saturday, August 4, 1:00 PM – 4:00 PM

Assessment CPE Video Podcasts and Blogs "Hands-on:" Consult, Publish & Teach Forensic Auditing, Expert Witness Testimony & Microsoft® Office Accounting Computer Litigation Support

(Specialized Knowledge & Applications)

Desc= ription/Objectives:
Repurpose DVD-recorded lectures using new Blu-ray® and HD-DVD® in t= he classroom and CPE, for the CPA, CMA, CIA, CISA/M WWW certificate Preps. Demonstrate download of the Microsoft Office Accounting, and upload the lec= ture on iTunesU®, Google Video®, and YouTube= ®. Review test automation compliance with NASBA, AACSB, COSO, Sarbanes-Oxley, HIPAA, and Basel II. Publish for students, alumnus, professionals, and the general public. Web cast using multi-media streaming tools to be more effec= tive and efficient, and provide a more enjoyable experience. Use the new Microsoft® Office 2007 Accounting, and Vista® involving Effective Learning Strategies, Emerging and Innovative Research, and Internet Research Forums. Instructors encourage existing and prospective faculty and students= to be involved and contribute in Assessing Submissions, Publications, Faculty Development, Teaching, Research, Practice, Service, Awards, and Placement s= treamline automation and SEO (Search Engine Optimization).

Pres= enters:
Avi Rushinek, University of Miami
Sara Rushinek, University of Miami

Spon= sor:
Teaching and Curriculum Section.

Note= : CPE fields of study are in parentheses

Rushinek, A. and R= ushinek, S. “How to Use New Media and Technology to Repurpose Classroom Instruction for the Accounting and Business Educator” 2008 Internatio= nal Fraud and Forensic Accounting Education Conference, May 9, 2008, Las Vegas, Nevada http://www.docstoc.com/docs/5525868= /2008-International-Fraud-and-Forensic-Accounting-Education-Conference<= /span>

Rushinek, A. and R= ushinek, S. "Mock Trial & Forensic Faculty

WWW Marketplace, J= ury, Judge & Attorneys "Courtroom & Classroom Instructional Technol= ogy Research & Service," American Accounting Association Annual Meeting 2008, Anaheim, California, CPE Session 2: Saturday, August 2, 8:00 AM-11:00= AM, sponsored by Teaching and Curriculum Section. http://aaahq.org/AM2008/cpe/= cpe02.htm

CPE Session 2: Sat= urday, August 2, 8:00 AM – 11:00 AM

Mock Trial & F= orensic Faculty

WWW Marketplace, J= ury, Judge & Attorneys — Courtroom & Classroom Instructional Technology Research & Service

(Accounting - Basi= c)

Description/Object= ives:

The workshop deals= with: E-Discovery Testimony, Accounting Corporate Information Unauthorized Disclosure, Identity Theft, Money Laundering, Fraud investigations, Office Accounting Cops & Robber Simulations, Phony Sales & Fraud detection= s, Spreadsheet Database Research Tracking, Digital Media Evidence CDs & DV= Ds, CYBER CRIME, Security ethical hacking anti-Virus controls, Cell Phones & PDAs Evidence, Trade Secrets Alleged Theft, Electronic Evidence Management. Threats Software & Hardware tool Integration Scenarios Deposition Preps, Jury Deliberation Litigation Arbitration & Mediation Industry Specific Academia, Hedge Fund, Government & Medical Shenanigans, Plagiarism, The= ft & Misrepresentation Controls Red-flags White-color Schemes, Scams Cons Crooks Drug & Human Trafficking Certification Qualification Monitor Confessions Boiler-Plate Rooms, Schemes, Counsels, Judges, & Lawyers Go= ne Wild (900)Sue-U2.com Foreign Offshore Banking, Tax Avoidance Evasion, & Wealth Management PI Family Divorce Real-estate Stock-Market Litigations Hi= red Guns Cross Examination Structured Settlements.

Advance Preparatio= n:

Email and or call presenters (arush@miami.edu, arushine@aol.com, (305) 666-7890) about cases, articles, issues, or any relevant suggestions and/or materials, and/or ideas for discussion and analysis during the workshop to presenters. Dr. Avi Rushinek, University of Miami, Accounting Department, Jenkins 421, C. Gable= s, Fl, 33124 USA (Run)Air-Webs/Phone (Cell) U. Miami, 5250 University Drive, C.I.S. Dept. Jenkins 421, C. Gables, Fl, 33124 (786)247-9327/7466 (Cell) Ac= c. Dept. 314 Jenkins, UM, Coral Gables, Fl, 33124, USA or 1205 Mariposa Ave. #= 208, Coral Gables, Fl, 33146.

http://www.onafree= .com/default.aspx

http://drjd.net/de= fault.aspx

http://dvdu.org/de= fault.aspx

http://moax.org/de= fault.aspx

http://www.webjobn= et.com

Presenters:=

Avi Rushinek, Univ= ersity of Miami

Sara Rushinek, Uni= versity of Miami

__________________= ______________________

Note: CPE fields o= f study are in parentheses

CPE Session 2: Saturday, August 2, 8:00 AM – 11:00 AM

Mock Trial & Forensic Faculty
WWW Marketplace, Jury, Judge & Attorneys — Courtroom & Classr= oom Instructional Technology Research & Service

(Accounting - Basic)

Desc= ription/Objectives:
The workshop deals with: E-Discovery Testimony, Accounting Corporate Information Unauthorized Disclosure, Identity Theft, Money Laundering, Fraud investigations, Office Accounting Cops & Robber Simulations, Phony Sales & Fraud detections, Spreadsheet Database Research Tracking, Digital Med= ia Evidence CDs & DVDs, CYBER CRIME, Security ethical hacking anti-Virus controls, Cell Phones & PDAs Evidence, Trade Secrets Alleged Theft, Electronic Evidence Management. Threats Software & Hardware tool Integration Scenarios Deposition Preps, Jury Deliberation Litigation Arbitration & Mediation Industry Specific Academia, Hedge Fund, Governm= ent & Medical Shenanigans, Plagiarism, Theft & Misrepresentation Contro= ls Red-flags White-color Schemes, Scams Cons Crooks Drug & Human Trafficki= ng Certification Qualification Monitor Confessions Boiler-Plate Rooms, Schemes, Counsels, Judges, & Lawyers Gone Wild (900)Sue-U2.com Foreign Offshore Banking, Tax Avoidance Evasion, & Wealth Management PI Family Divorce Real-estate Stock-Market Litigations Hired Guns Cross Examination Structured Settlements.

Adva= nce Preparation:
Email and or call presenters (arush@miam= i.edu, arushine@aol.com, (305) 666-7890) a= bout cases, articles, issues, or any relevant suggestions and/or materials, and/= or ideas for discussion and analysis during the workshop to presenters. Dr. Avi Rushinek, University of Miami, Accounting Department, Jenkins 421, C. Gable= s, Fl, 33124 USA (Run)Air-Webs/Phone (Cell) U. Miami, 5250 University Drive, C.I.S. Dept. Jenkins 421, C. Gables, Fl, 33124 (786)247-9327/7466 (Cell) Ac= c. Dept. 314 Jenkins, UM, Coral Gables, Fl, 33124, USA or 1205 Mariposa Ave. #= 208, Coral Gables, Fl, 33146.

http://www.onafree.com/defa= ult.aspx
http://drjd.net/default.aspx http://dvdu.org/default.aspx http://moax.org/default.aspx http://www.webjobnet.com

Pres= enters:
Avi Rushinek, University of Miami
Sara Rushinek, University of Miami

Note= : CPE fields of study are in parentheses

Rushinek, A. and R= ushinek, S. "Role Play Forensic Accounting, Auditing & Tax Expert Witness: Providing Testimony and Computer Litigation Support Teaching, Services, Research and Development," American Accounting Association Annual Meeting 2008, Anaheim, California, CPE Se= ssion 11: Saturday, August 2, 1:00 PM-4:00 PM, sponsored by Teaching and Curricul= um Section.

http://aaahq.org/AM2008/cpe/= cpe11.htm

CPE Session 11: Sa= turday, August 2, 1:00 PM – 4:00 PM

Role Play Forensic Accounting, Auditing & Tax Expert Witness:

Providing Testimon= y and Computer Litigation Support

Teaching, Services, Research and Development

(Accounting - Basi= c)

Description/Object= ives:

The workshop deals= with: Rogue Wireless Access Points, Internal Threat Management, Vulnerability Exploits Web, Intrusion Detection, Eaves Dropping AV, Surveillance Recording Audit, On Demand Live Consulting, Internet Access Data points, Consumer Electronics Gone Wild, IT (Information Technology) Governance, Log Forensics Stealth Regulatory Compliance Audit Trails, Mobile Device Digital Consumer Electronics Transcription Stenography, voice to text automation, Cell Phone Video Cam Justice & Court TV Shows Camera Imaging Distribution, Lawsuit Incident Response, WWW Network URL TCP/IP HTTP, Stealth Data Intelligence Office Accounting Information Systems Demos "But-for/If" Analytics Metrics Trap Door Trojan Horses, Spying & Due Diligence Enterprise Risk & Damage Assessment Market Rates & Charges Malpractice & Busine= ss Valuation "Lost Profits" Board of Directors Litigation Service Confessions Acting Telling a Story Sound-byte T= ips & Tricks Interrogate Terror Home-land Security Bribery Black-mail Corruption & Where is the Money Laundering.

Advance Preparatio= n:

http://www.onafree= .com/default.aspx

http://drjd.net/de= fault.aspx

http://dvdu.org/de= fault.aspx

http://moax.org/de= fault.aspx

http://www.webjobn= et.com

Presenters:=

Avi Rushinek, Univ= ersity of Miami

Sara Rushinek, Uni= versity of Miami

__________________= ______________________

Note: CPE fields o= f study are in parentheses

Overview

Registration

Hotel Info

Advertising

Exhibitors

CPE Session 11: Saturday, August 2, 1:00 PM – 4:00 PM

Role Play Forensic Accounting, Auditing & Tax Expert Witness:
Providing Testimony and Computer Litigation Support
Teaching, Services, Research and Development

(Accounting - Basic)

De= scription/Objectives:
The workshop deals with: Rogue Wireless Access Points, Internal Threat Management, Vulnerability Exploits Web, Intrusion Detection, Eaves Droppi= ng AV, Surveillance Recording Audit, On Demand Live Consulting, Internet Acc= ess Data points, Consumer Electronics Gone Wild, IT (Information Technology) Governance, Log Forensics Stealth Regulatory Compliance Audit Trails, Mob= ile Device Digital Consumer Electronics Transcription Stenography, voice to t= ext automation, Cell Phone Video Cam Justice & Court TV Shows Camera Imag= ing Distribution, Lawsuit Incident Response, WWW Network URL TCP/IP HTTP, Ste= alth Data Intelligence Office Accounting Information Systems Demos "But-f= or/If" Analytics Metrics Trap Door Trojan Horses, Spying & Due Diligence Enterprise Risk & Damage Assessment Market Rates & Charges Malpractice & Business Valuation "Lost Profits" Board of Directors Litigation Service Confessions Acting Telling a Story Sound-byte Tips & Tricks Interrogate Terror Home-land Security Bribery Black-mail Corruption & Where is the Money Launderin= g.

Ad= vance Preparation:
Email and or call presenters (arush@mi= ami.edu, arushine@aol.com, (305) 666-7890)= about cases, articles, issues, or any relevant suggestions and/or materials, an= d/or ideas for discussion and analysis during the workshop to presenters. Dr. = Avi Rushinek, University of Miami, Accounting Department, Jenkins 421, C. Gab= les, Fl, 33124 USA (Run)Air-Webs/Phone (Cell) U. Miami, 5250 University Drive, C.I.S. Dept. Jenkins 421, C. Gables, Fl, 33124 (786)247-9327/7466 (Cell) = Acc. Dept. 314 Jenkins, UM, C. Gables, Fl, 33124, USA.

http://www.onafree.com/de= fault.aspx
http://drjd.net/default.aspx=
http://dvdu.org/default.aspx=
http://moax.org/default.aspx=
http://www.webjobnet.com

Pr= esenters:
Avi Rushinek, University of Miami
Sara Rushinek, University of Miami

No= te: CPE fields of study are in parentheses

Rushinek, A. and R= ushinek, S. “How to Use Cases, Role Playing and Mock Trials in the Classroom: The Role of the Accounting and Business Educator with the Aid of= New Computer Media,” American Accounting Association Annual Meeting 2008, August 6, 2008, Anaheim, California http://aaahq= .org/TeachCurr/newsletters/Summer2008.pdf

"Tele-Medicin=
e's SMART Surveillance AV HD IP Security Network Camera Audit Systems,=
 '"  Jay Weiss Center for Social Medicine and Health Equit=
y, University of Miami Miller School of Medicine, 2009 JWC SMART Forum, Loi=
s Pope LIFE Center, April 24,2009, Miami, Florida. http=
://aaahq.org/AM2009/Emerging.cfm

Rushinek, A. and Rushinek S. "IFRS Compliant Real-Time 2-Way AV (Audio/Vid= eo) HD (High Definition) Recording Audit of Effective Learning Strategies for Forensic IT Networks of Expert Witnesses Research that focuses on Investiga= tive Accounting Testimony, & Computer Litigation Support," 2009 AAA American Accounting Association, Annual Meeting Proceedings, August 1-5, 2009, New Y= ork, New York.

Rushinek, A. & Rushinek, S., (2009, <= span style=3D'color:black'>August). "IFRS Compliant Real-Ti= me 2-Way AV (Audio/Video) HD (High Definition) Recording Audit of Effective Learning Strategies for Forensic IT Networks of Expert Witnesses Research t= hat Focuses on Investigative Accounting Testimony, & Computer Litigation Support, Effective Learning Strategies" 2009 American Accounting Association (AAA) Annual Meeting = NY, NY., Monday August 3, 2009, retrieved 10-27-2009 http://aaahq.org/AM2009/ELS01.cf= m

IFRS (International Financial Reporting Standards) Compliant Real-Time 2-Way AV (Audio/Video) HD (High Definition) Recording Audit of Effective Learning Strategies or Forensic IT Networks of Expert Witnesses Research that Focuse= s on Investigative Accounting Testimony, & Computer Litigation Support Presenters: Avi Rushinek, University of Miami; Sara Rushinek, University of Miami

Description: Effective Learning Strategies (ELS) that repurposes stand-up traditional classroom instruction= AV (Audio Video) surveillance recorded to a DVD, for real-time auditing, dista= nce learning, CPE courseware, as well as Tutor CDs and DVDs. As soon as the cla= ss is over, the instructor's PC uploads the AV session to Google Video, and to= an WWW DVD-burner.

Rushinek, A. and Rushinek S. "Cell Phone Real Time Forensic Audit Of Tele-Medicine e-Bill Records over the WWW AV IP Network Security Surveillan= ce Reconnaissance PTZ Cam Systems, CCTV & DVD-Recording: Repurposed Stand-= Up Instructions for Distance or Blended Education and CPE," = 2009 AAA American Accounting Association, Annual Meeting Proceedings, August 1-5, 2009, New Y= ork, New York. http://aaahq.org/AM2009/Emerging.cfm

Rushinek, A. & Rushinek, S., (2009, <= span style=3D'color:black'>August). "Cell Phone Real Time Forensic Audit of Tele-Medicine e-Bill Records over the WWW AV IP Network Security Surveillance Reconnaissance PTZ (Pan Tilt & Zoom) Cam Systems, CCTV (Closed Circuit TV) & DVD-Recording: Repurposed Stand-Up Instructi= ons for Distance or Blended Education and CPE, Emerging and Innova= tive Research Session" 2009 American Accounting Association (AAA) Annual Meeting NY, NY., Tuesday August 4, 2009, retrieved 10-27-2009 http://aaahq.org/AM2009/Emerg= ing.cfm

Cell Phone Real Time Forensic Audit of Tele-Medicine e-Bill Records over the WWW= AV IP Network Security Surveillance Reconnaissance PTZ Cam Systems, CCTV & DVD-Recording: Repurposed Stand-Up Instructions for Distance or Blended Education and CPE (Continuing Professional Education) Presenters: Avi Rushinek, University of Miami; Sara Rushinek, University of Miami, Description: This is a study of cell phone real time auditing. To complement the traditi= onal after-the-fact auditing, this case study deals with real-time auditing. Meaning, that the auditor watches the activities in real-time. It is using = the World Wide Web WWW internet instead of limiting the access to the intranet; adding profitable online distance learning.

Rushinek, A. and Rushinek S. "Health Care Forensic Information Technology Networks of Expe= rt (FITN) Witnesses Teams Researching Investigative Tax, Accounting & Audi= ting (FIA) Forecasts Archives of Testimony, Assessment, Publishing, Teaching, Service, & WWW Multi-Media Consulting," 2009 AAA American Accounting Associatio= n, Annual Meeting Proceedings, August 1-5, 2009, New York, New York.

Rushinek, A. & Rushinek, S., (2009, <= span style=3D'color:black'>August). "Health Care Forensic Information Technology Networks of Expert (FITN) Witnesses Teams Researching Investigative Tax, Accounting and Auditing (FIA) Forecasts Archives of Testimony, Assessment, Publishing, Teaching, Service, and WWW Multi-Media Consulting" 2009 American Accounting Association (AAA) Annual Meeting NY, NY., Tuesday August 4, 2009, retrieved 10-27-2009 http://aaahq.org/AM2009/Emerg= ing.cfm He= alth Care Forensic Information Technology Networks of Expert (FITN) Witnesses Te= ams Researching Investigative Tax, Accounting and Auditing (FIA) Forecasts Arch= ives of Testimony, Assessment, Publishing, Teaching, Service, and WWW Multi-Media Consulting, Presenters: Avi Rushinek, University of Miami; Sara Rushinek, Universit= y of Miami,

Description: Emerging and Inno= vative Research highlights promising Forensic IT (Information Technology) Networks= of Expert (FITN) Witnesses Research that focuses on Forensic Investigative Accounting and Auditing (FIA) research ideas. Authors describe their work in progress soliciting others to research it and help them.

//////////////////////////////////////////////////////////////

Avi Rushinek &nbs= p; &= nbsp; &nbs= p; &= nbsp;

University of Miami,

Coral Gables, Fl 33124, USA

E-mail: arush@miami.edu

E-mail: arushine@aol.com

Sara Rushinek*

Department of Accounting, School of Business Administration,

1120 Stanford Drive, Coral Gables Fl 33124, USA

Fax: 305.666.7890

E-mail: s.rushinek@miami.edu

*Corresponding author

Abstract: This paper studies the sub-prime mortgages, credit default swaps

(CDS) and collateralised synthetic obligations (CSOs) cooking the books of the

financial services industries at a global level. The paper uses the case of the<= /o:p>

Bank of Barclays to develop a methodology of uncooking the books from=

material misstatements of the financial industry. This research is about the

current sub-prime markets crisis and shows how sub-prime mortgages, CDS<= /span>

and CSOs overstated the revenues of the financial services industries leading t= o

the stock markets meltdown of October 2008. The paper attempts to develop<= /o:p>

regression models and software that detect fraud involved in the mortgages<= /span>

crisis and a method for fraud ‘finger print’ definition. This paper pr= ovides the

readers with fundamental information about firms and auditors’ misconduct.

While this study researches primarily in the area of international economics and<= o:p>

accounting, it also deals with computer information systems automation and

specifically forensic accounting, expert witness testimony and computer

litigation support.

Copyright © 200x Inderscience Enterprises Ltd.

Keywords:

Comment [t1]: <= /span>Author: Please

provide up to 10 keywords.

Reference to this paper should be made as follows: Rushinek, A. and

Rushinek, S. (xxxx) ‘Uncooking the books from toxic paper sub-prime<= /span>

mortgages CDS and CSOs material misstatements of the financial services

industry: a Barclays Bank PLC case study’, Int. J. Economics and Accounting,<= /o:p>

Vol. X, No. Y, pp.000–000.

Biographical notes: Avi Rushinek is an Accounting Professor at the

University of Miami. He obtained his PhD from The University of Texas at

Austin. His research interests include e-commerce security and controls, health

insurance portability and accountability (HIPAA), Sarbanes-Oxley and Basel II

compliance forensic accounting, web marketing ROI, e-learning and internet<= /span>

domain copyright, trademarks and patents ROI.

2 A. Rushinek and S. Rushinek<= o:p>

Sara Rushinek is a Professor of Computer Information Systems at the

University of Miami. She obtained her PhD from The University of Texas at

Austin. His research interests include business intelligence and data mining for

decision-making, electronic stock trading; e-learning, computer forensics and

e-commerce security of enterprise resource management.

1 Introduction

It is evident that past stock market meltdowns could not rely on financial reports. The

‘blame game’ once again appeared after the fact. Reminiscent of the fall of Enron, lax

auditing and management misconduct allegations were reported. Again, it seemed the

problem was the failure of financial reports to report a crucial fact: a company’s actual

financial condition.

The mortgage credit crisis evolved into a worldwide financial panic in 2007. Feeding

the panic was a lack of information about which lenders were at most risk, how = many

‘toxic’ loans exist and where the next surprise would emerge.

Michael Missal, a US Bankruptcy Court examiner accused KPMG of lax auditing and

some former New Century executives could be legally liable for millions of dolla= rs in

damages because of their conduct. Missal (2008) states in his report that “…Driven by a

‘brazen obsession’ with generating sub-prime mortgages, Irvine’s New Century Financial

Corp. engaged in improper accounting that overstated its profit and allowed top

executives to reap millions of dollars in inflated or undeserved bonuses”.<= /o:p>

Michael J. Missal’s report said senior managers ‘turned a blind eyeR= 17; to the ‘ticking

time bomb’ created by the high-risk lending in 2005 and 2006. At the same time, Missal

(2008) reports that “New Century’s auditor, KPMG, contributed to the problems by

failing to exercise due care in reviewing its books, leading to material misstateme= nts in

New Century’s financial reports”.

This was a sweeping accusation against one of the largest accounting firms that<= o:p>

improper and imprudent practices by a once high-flying mortgage company were

condoned and enabled by its auditors. KPMG denied the accusations in the report, whi= ch

was commissioned by the US Trustee overseeing the New Century bankruptcy

(Bajaj, 2008).

2 Credit default swaps (CDS)

At the time of the financial crisis Monks (2008) wrote that mortgage bailouts could

trigger massive credit default swap settlements. The government takeover of Fannie = Mae

and Freddie Mac could trigger the largest credit default swap settlement ever. Actual

payments could be limited, however, as a result of the relatively high value of the<= o:p>

mortgage underwriters’ bonds. Investors may be forced to settle contracts cove= ring the

mortgage giants’ $1.6 trillion in outstanding debt because the government seiz= ure

constitutes a credit event that triggers the payment or delivery of their bonds. The

International Swaps and Derivatives Association announced that it would establish a<= /o:p>

protocol to facilitate the settlement of CDS trades involving Fannie Mae and Freddie=

Mac.

Uncooking the books from tox= ic paper sub-prime mortgages 3<= /o:p>

Monks (2008) reported that J.P. Morgan analyst Eric Beinstein said that “th= is will

likely be the largest CDS credit event in terms of the amount of CDS contracts

outstanding. The settlement process will likely take 30 days, with investors cashing the= ir

CDS contracts at a price established through an auction process. Payouts could = be

limited, though, because most analysts believe CDS covering the mortgage companies’

senior debt will be settled”. In a cash CDS settlement, buyers are paid the difference

between the par value and market value of the debt obligation.

2.1 Fannie, Freddie CDS may = be settled

Biggadike and Harrington (2008) write that – “Investors may be forced to settle contracts

protecting more than $1.4 trillion of Fannie Mae and Freddie Mac bonds against default=

after the U.S. seized control of the companies in a bid to bolster the housing market.

(Thirteen ‘major’ dealers of credit-default swaps agreed ‘unanimously’ that the rescue

constitutes a credit event triggering payment or delivery of the companies’ bonds, the

International Swaps and Derivatives Association said in a memo obtained by Bloomberg=

News. Market makers for the privately traded contracts will discuss how to settle them in

a conference call. The market is not experienced at settling a credit event f= or a name of

this size, so it is a bit of an unknown. A settlement likely would be the larges= t in the

market’s decade-long history. Credit-default swaps on Fannie and Freddie have been

among the most actively traded the past few months, according to reports from bro= ker

GFI Group Inc. Both companies also are among 125 companies in the benchmark Mar= kit

CDX North America Investment Grade Index, the most actively traded contract in credit

markets, which investors use to speculate on corporate creditworthiness or to hedge<= o:p>

against losses”.

2.2 Conservatorship is a cre= dit event Barclays PLC analysts note to clients

Harrington and Unmack (2008) report that “We believe conservatorship is a credit event,

Barclays PLC analysts Vince Breitenbach and Jeff Meli said in a note to clients. Barclays

is a member of the ISDA. US default protection costs as measured by the Markit= CDX

North America Investment Grade Index will also decline. Harrington and Unmack (20= 08)

to write that “No one knows exactly who has what at stake because there’s no central

exchange or system for reporting trades. Sellers are required to post collateral, or pledge

assets, to the buyer of protection, known as the counterparty, on the other side of= the

trade if the value of their positions declines.”

Hedge funds, insurance companies and banks typically buy and sell credit protecti= on,

which is used either to insure a bond against default or as a bet against the company’s

ability to pay its debt. Some funds may be forced to dump assets to meet the paymen= t

demands if they have not hedged, But fund managers or hedge funds, once they have

used their cash, have only one option: to sell assets.

According to Harrington and Unmack (2008) the failures of Lehman, once the fourth

largest securities firm, and Seattle-based Washington Mutual Inc. as well as the

government takeovers of Fannie Mae, Freddie Mac and Iceland’s biggest banks have=

provided the ten-year-old CDS market with its biggest test to date.

In the same article by Harrington and Unmack (2008) cited James Pickel, the he= ad of

the International Swaps and Derivatives Association as stating that CDS contrac= ts did not

cause any firm to fail, the underlying cause of problems that has affected firms = is the risk

4 A. Rushinek and S. Rushinek<= o:p>

that they chose to take on. CDS are financial instruments that can be based on b= onds and

loans. They pay the buyer face value in exchange for the underlying securities or = the cash

equivalent should a borrower fail to adhere to its debt agreements.<= /p>
As reported by Herman and Shields (2008) the bankruptcy court approved the sal= e of

Lehman broker dealer to Barclays. They proceed to report: “Questions raised about

Lehman’s transfer of assets among its businesses in the days before its bankruptcy filing

did not interfere with swift action by the bankruptcy court to approve the sale= of

substantially all of the assets of Lehman Brothers, Inc., to Barclays Capital. The court’s

decision followed a marathon eleven-hour hearing in a packed Manhattan courtroom

where attorneys from the SEC and other government agencies successfully supported=

Lehman’s argument that swift approval of the deal was in the national interest. The<= o:p>

national interest apparently is the prompt transfer of the broker-dealer’s customer

accounts instead of a lengthy brokerage liquidation process. The transfer of most re= tail

accounts, which hold over one hundred billion dollars in assets, is expected to be

completed within days. In one of the last liquidations of a major securities firm, wh= en

Drexel collapsed in 1990, it was weeks before customer accounts were transferred t= o a

new firm. The expeditious transfer of Lehman’s assets also avoids disrupt= ion of capital

markets because securities transactions will continue to be completed and Lehman’s

counterparties can confidently continue to do business with the firm”.

2.3 Barclays’ auditors report that the financial statements are free from

material misstatements<= /o:p>

At the time of the mortgage crisis, the auditors were still stating that the financial

statements are free from material misstatements. According to Barclays PLC annual=

report of 2007, quoting from the Report of Independent Registered Public Accountin= g

Firm to the Board of Directors and Shareholders of Barclays Bank PLC, the accountants

stated:

“In our opinion, financial statements are free of $$ material misstatement.

PricewaterhouseCoopers LLP, Chartered Accountants and Registered Auditors,

London, United Kingdom, 10th March 2008, proceed in the ‘Internal controlR= 17;

section of Barclays report they repeat the assertion that they provide

‘reasonable assurance against $$ material misstatement or loss’.”

On page 148 of Barclays, Annual Report 2007, the auditors use the same boilerp= late

stating that: “Internal control systems obtain reasonable assurance about whe= ther the

financial statements are free of material $$ misstatement” (Barclays, 2007).

2.4 Implying that CDOs are s= afe assets and understating the toxic inherent risk

In the ‘corporate bonds’ section Barclays Annual Report of 2007 it states that “Corporate

bonds are generally valued using observable quoted prices or recently executed

transactions. Where observable price quotations are not available, the fair value is=

determined based on cash flow models where significant inputs may include yield curves= ,

bond or single name credit default swap spreads”. They never mention any of the reports

on the internet concerning the toxic paper and the debate in the press.

In the ‘Derivatives’ section of Barclays 2007 annual report, furth= er explanations are

that... “Derivative contracts can be exchange traded or over the counter (OTC= ). OTC

Uncooking the books from tox= ic paper sub-prime mortgages 5<= /o:p>

derivative contracts include forward, swap and option contracts related to interest ra= tes,

bonds, foreign currencies, credit standing of reference entities, equity prices, f= und levels,

commodity prices or indices on these assets. For many pricing models, there is no

material subjectivity because the methodologies employed do not necessitate signific= ant

judgment and the pricing inputs are observed from actively quoted markets, as is the case

for generic interest rate swaps and option markets. In the case of more establi= shed

derivative products, the pricing models used are widely accepted and used by the other=

market participants”, implying that they are safe assets and understating the toxic

inherent risk.

2.5 Misrepresent reality by applying ‘Monte Carlo simulation is used rather=

than analytic approximation’ where totally unrealistic assumptions can

overstate the performance and understate the toxic paper true risk exposure

In Barclays (2007) Annual Report in the ‘Structured credit derivatives’ section of the

report states that: “Collateralised synthetic obligations (CSOs) are structu= red credit

derivatives which reference the loss profile of a portfolio of loans, debts or syntheti= c

underlyings. The reference asset can be a corporate credit or an asset backed credit. Fo= r

CSOs that reference corporate credits an analytical model is used. For CSOs on a= sset

backed underlyings, due to the path dependent nature of a CSO on an amortising

portfolio a Monte Carlo simulation is used rather than analytic approximation. The

expected loss probability for each reference credit in the portfolio is derived from= the

single name credit default swap spread curve and in addition, for ABS references, = a

prepayment rate assumption.

A simulation is then used to compute survival time which allows us to calcula= te the

marginal loss over each payment period by reference to estimated recovery rates.

Significant inputs include prepayment rates, cumulative default rates, and recovery

rates.” Again, they misrepresent reality by applying ‘Monte Carlo simulation = is used

rather than analytic approximation’ where totally unrealistic assumptions can overstate

the performance and understate the toxic paper true risk exposure In the ‘derivatives’

section the report continues to confuse the issues and understate the risk by saying that

“Derivative contracts can be exchange traded or over the counter (OTC). OTC derivative<= o:p>

contracts include forward, swap and option contracts related to interest rates, bonds= ,

foreign currencies, credit standing of reference entities, equity prices, fund leve= ls,

commodity prices or indices on these assets” (Barclays, 2007).

So one can see, history does seem to repeat itself. Financial reporting could = not

always portray the financial health of the markets. Sub-prime mortgages, CDS and C= SOs

overstated the revenues of the financial services industries leading to the stock mark= ets

meltdown of October 2008.

2.6 Net Sales/total assets estimating 10% inflated net sales and receivable, to<= /p>
increase gross profit and st= ock market values: a simulated banking fraud

software case study

2.6.1 Overview

This study finds the ratios net sales/total assets, SG and A/sales and net sales/employees

as the best predictor percent differenced financial ratios (BP%DFR) and most effective in

6 A. Rushinek and S. Rushinek<= o:p>

constructing a fraud cost model. These BP%DFR variables are estimating 10% inflated=

net sales and receivables, to increase gross profit and stock market values fra= ud value.

We use these variables to build a regression model. This is a case study of fBarclays

Bank PLC (f =3D fictitiously simulated fraud, contrary to r =3D real) and the co= mmercial

banks. We decompose fraud values into fixed fraud (FF), variable fraud (VF) and th= eir

sum mixed fraud (MF), building a regression model for each component. Thus, we<= o:p>

develop a cost function (CF) for each component, fixed fraud cost function (FFCF),<= o:p>

variable fraud cost fraud (VFCF) and mixed fraud cost function (MFCF).

We simulated fraud (SF) of the net sales, which is the primary (1st) account. = We

balance this 1st account against the receivables account, the 2nd account. The FF remains

fixed starting from the 1st period, 12/31/85, to the last period, 12/31/94, at $1,318,800.

We have calculated the FF as greater than 10% of the 1st primary SF account or= 1% of

net sales. Add the FF to the VF and you get the MF for the last year $2,637,600. Add this

MF to the initial balance of the 1st account, the real account balance. This r= NET SALES

balances $13,188,000, for the last period of this study. This way, you get the phony=

(f =3D fraudulent) balance of 1st account, fNET SALES, $15,825,600. Likewise, = for the 2nd

balancing account, start with the real balance, (r prefixed account), $115,356,000,

combine the SF, $2,637,600 and you get the phony value (f prefixed) of the

fRECEIVABLES balancing the SF at $117,993,600.

Comment [t2]: <= /span>Author: Please

reconstruct the sentence to make the

intended meaning clear to readers.

We difference (deduct) the fNET SALES (fraudulent, post SF) from the, rNET

SALES (real, pre SF), calculating the SF dollar values. Likewise, we difference a= ll the

financial ratios (%DFR), to identify the fraud drivers. We regress the SF dollar valu= es on

the %DFR, to discover the best [highest R-square (RS)] predictor percent differenced

financial ratios (BP%DFR). In this case, these (BP%DFR) include: net sales/total asse= ts,

SG and A/sales and net sales/employees.

2.6.2 Introduction of %DFR

According to new securities legislation, auditors will need to report more quickly th= an at

present any suspicion of fraud. A tougher approach is also coming from the auditing=

standards board of the American Institute of Certified Public Accountants<= /span>

(AICPA, 1988). The new standard will require external auditors to be more aggressiv= e in

not only reporting but unearthing fraud and faulty financial statements. Failur= e to comply

with the institute’s measure will include civil penalties and possible los= s of a CPA’s

license. These proposed standards will have auditors spotting higher risk of fraud t= o

develop specific plans to eliminate that risk. The board has been considering stiffening

auditing standards for several years as research showed that current standards were = not

tough enough on discovering major frauds (Berton, 1996). Congress has also considering

a bill would create a Financial Services Oversight Council made up of the Treasury

secretary, the Federal Reserve chairman and heads of regulatory agencies to monitor th= e

financial markets for potential threats to the US system. It would identify firms and=

activities that should be subject to heightened standards, including requirements that they

place more money in reserve. Companies would have to plan for their own demise,

detailing how they would be dismantled if they failed (Kuhnhenn, 2009).

This study defines the %DFR as a predictor variable for estimating the value of fraud.

We are developing several theories and applying other theories to design this = study. The

reversed accounting theory (RAT) contends that to discover fraud, we have to go from=

the final financial statements, in reverse, to the original fraudulent transact= ion. To pick

Uncooking the books from tox= ic paper sub-prime mortgages 7<= /o:p>

the proper methodology, we are applying activity based costing (ABC) to RAT and fraud,

developing a derivation of ABC, activity based fraud (ABF). ABF contends that like any=

other cost, fraud originates from some activities. Since we do not know what thes= e

activities are, we could use highly correlated surrogates to these activities to act as fraud

drivers. These %DFR are such drivers.

We intend to supplement existing expert systems (ES) software that can discriminate

between fraudulent and fraud free companies, but cannot pinpoint the amounts and th= e

accounts. Integrating such a model into these ES, will extend their abilities beyond<= o:p>

current technology. Such extension will enable the ES to quantify the fraud and flag its

sources (accounts). The supplementary nature of model we develop explains its<= /o:p>

limitations. This model is not for discovering fraudulent companies, since it will be

redundant to the existing ES. Thus, we optimise it for a relevant range, that outside= the

vicinity of the origin, where both the %DFR and the fraud approach a value of zero.<= o:p>

Therefore, we hypothesise that the intercept will be statistically insignificant about= the

origin, which it turns out to be. In contrast to the insignificance of the intercep= t, we

hypothesise that the entire model will be significant, as well as at least one PERCENT.=

Indeed, the analysis of variance (ANOVA) confirms our expectations, rejecting the n= ull

hypothesis that the model is insignificant and that the regression coefficients are eq= ual to

zero.

Since we intend to use it ultimately on a fraud suspect (a company that ES has

identified), we refer to that company as the focus company. In contrast, the industry or=

competitors’ ratio averages constitute a fraud free peer review group (PRG). We calculat= e

the percentage as the ratios of the focus less the peers divided by the peers. These

assumptions that fraud is the single difference between the focus and the peers highlig= ht

the limitations of this approach. These limitations exclude heterogeneous industries

where companies are not very similar, as well as countries where fraud (e.g., bri= bes) is

part of doing business and generally accepted accounting principles and auditing=

standards (GAAP/S) are rare.

We search for the ratios that will maximise the correlation between the %DFR, = the

predictor variable(s) and the SF, the predicted variable. We find that these ratios c= an help

in auditing, detecting and deterring fraud, as well as help develop rules for = the ES

software for analytical review (ARES). We apply it to one company and one industry, = but

design it to be more generic, so we can expand it to other companies and industrie= s

(Rushinek and Rushinek, 2000, 2003). This study simulates fraud (SF) in the financial=

statements. This SF combines VF and FF into an MF. We regress, correlate and=

decompose such an MF into its VF and FF components. We compare accounts, ratios,=

periods and companies. We regress a focus company with SFs on its PRG average<= /o:p>

(assumed to be without the same MF). We apply RAT to ES software and then test the

hypothesis that RAT can decompose MF and help in flagging the MF’s riskiest financial

ratios. Eventually, the riskiest ratios will be part of the riskiest accounts.=

Applying RAT, we decompose the MF into its VF and FF components. To

decompose the MF, we regress it on sales. We define the FF as regression’s intercept and

the VF as the regression’s slope. Using the least square regression, we construct the MF

function. We forecast sales as a function of serial date, testing this date as a surrogate for

sales, whenever it is a good predictor of sales. We construct a statistically significant

fraud model. This study calculates risk for analytical review diagnostics controls for

automated personal computer (PC) expert systems (ARES) based RAT. We integrate

RAT and the decomposition of this MF into the design of ARES, demonstrating som= e

8 A. Rushinek and S. Rushinek<= o:p>

rules, screen and reports that will result from applying these ideas. By scanning financial

statements and their ratios and regressing them against the peers, averages the ARES

could fire some rules. These rules will quantify the risk of the likelihood of ov= er or under

stating balances of accounts, helping auditors plan their audit. We could use such = risk

measures to allocate audit time to accounts.

Likewise, such fraud coefficients help the ARES prioritise its output and rate its

opinions (related to problems in different accounts: phony sales ore receivable) from the

most to the least risky accounts. Thus, the ARES could minimise over loading the user

with reports that they cannot process. This way the users can limit the ARES to report

only on the top most risky accounts and/or ratios, or pages of reports. This will relieve the

users from evaluating hundreds of accounts and ratios. This is especially true wh= en the

users are not knowledgeable, do not have the resources to conduct such an evaluat= ion,

and cannot set apriority the materiality level. This could facilitate the deplo= yment of

ARES for inexperienced managers and auditors. Next, we would like to define the = SF and

some of its assumptions.

2.6.3 ABC and cost and management accounting theory (CMAT)

Applying ABC theories to fraud, we develop the ABF theory. ABF treats fraud as a cos= t

that we want to trace back to the activities that generate it, much like any cos= t. Except

that for fraud, that unlike traditional costs, which we simply want to minimise,= we want

to completely eradicate and eliminate, approximating a zero level fraud. To calculate the

cost effectiveness of fraud controls, we have to estimate it (since criminals ne= ver report

frauds voluntarily – unlike other costs), decompose it, leading to the activ= ity sources. For

that purpose, we deploy the RAT to trace the fraud back to its sources as a step= to

eliminate such fraud. Our unique contribution is going in ‘reverse’ to traditional auditing.

Instead of going from the individual transactions to the financial statements, we g= o in

reverse, from the financial statements trying to reconstruct fraudulent entries. Hen= ce, we

develop the concept of RAT. Much like reversed engineering, RAT goes in reverse to = the

common sequence of accounting work. We test the hypothesis that RAT can construct = a

statistically significant fraud function.

We deploy the CMAT and define VF, FF and MF, as well as develop software<= /o:p>

integrity controls. CMAT suggests that the slope of mixed cost (MC) regressed on the<= o:p>

sales denotes variable cost rate (VCR). Likewise, the intercept of MC regressed on sales

estimates the fixed cost (FC). Similarly, our dollar SF regressed on the sales has a slope

and an intercept. This slope shows the variable fraud rate (VFR), which is analogous to

the VCR. Likewise, the intercept (of the SF on sales) shows the FF, analogous to the FC.

RAT contends that whenever we are dealing with unknown fraud. Decomposing this<= o:p>

fraud (into its fixed and variable components) will be helpful in classifying its behaviour

and eventually pinpointing its sources.

Uncooking the books from tox= ic paper sub-prime mortgages 9<= /o:p>

3 Fraud simulation (FS) defini= tion

3.1 fBarclays Bank PLC net s= ales – FS definition

3.1.1 VF, FF and MF definiti= on

The 10% SF of the net sales primary (1st) account is balanced against the SF of= the

secondary (2nd) balancing receivables account. The FF remains fixed starting from the=

1st period, 12/31/85, to the last period, 12/31/94, at $1,318,800 amount. We ha= ve

calculated dollar fraud amount as 10% higher of the 1st primary SF account or 1% of ne= t

sales. Add the FF to the VF and you get the MF for the last year $2,637,600. Add t= his MF

to the initial balance of the 1st account, real, rNET SALES balance $3,188,000, for the

last period. You get the phony, fraudulent, balance of 1st account, fNET SALES,<= o:p>

$15,825,600. Likewise, for the 2nd balancing account, start with the real balance,<= /o:p>

rRECEIVABLES, $115,356,000. Then, combine the SF, $2,637,600, and you get the<= /span>

phony balance of the fRECEIVABLES balancing that SF at $117,993,600. Our question=

is which ratio is this fraud’s top predictor?1

3.2 fNET SALES fraud, why wo= uld criminals create it and what predicts it?

The objective and the result of such a fraud may be the inflated net sales and receivables,

to inflate gross profit and stock market values. However, this may not explain= why a

criminal would do it in a certain way. We could understand the possible purpose of s= uch

a fraud by looking at the financial results. An fBarclays Bank PLC Executive working in

the commercial banks industry (COB) may want to raise net income (reduce loss), credit

ratings, commissions, or promote his or her reputation. For example, a fraud that

overstates the sales balance, will in turn overstate the net income. Overstating net income

will make the company appear to be more profitable. Such an overstatement of the sales

and profitability of the company may benefit employees who own stock options, and/or

commission employees. An overstatement of sales may easily translate to a pay raise, f= or

certain employees. Misclassifying long term (LT) assets as current assets, can impr= ove

the credit rating of a company. However, such a fraud may not affect the income=

statement at all. We expect to find a ratio that can forecast such fraud.<= /span>

3.3 10% fNET SALES fraud: account and amount

A fraud perpetrator may prefer to misstate an account with a balance greater = than zero. It

is possible, that overstating an account with a positive balance may appear a = bit easier to

conceal, compared to an account with a zero balance. In the case of fBarclays Bank P= LC,

and the COB, the rNET SALES (r =3D real) account, is more likely to have a posi= tive

balance, compared to other companies and industries. An account such as cost of good= s

will have a positive (debit) balance. Unlike some other accounts, such as invest= ment

gains/losses, this is equally likely to have either balance, positive (debit), negative

(credit). Likewise, it is most likely to have a zero balance in a non-banking or fina= nce

environment. Once we have picked up an account, the next question is concerning the=

amount. The criminal (the perpetrator of the fraud) may not know in advance what th= e

normal balance of the account should be at all times. Therefore, a fraud that is a=

percentage of the real balance may appear safer (easier to conceal, automate and harde= r to

detect) choice. Likewise, small percentages such as 10%, as used in the present cas= e, may

10 A. Rushinek and S. Rushinek<= o:p>

appear to be safer than larger percentages. If the ‘criminals’ got away with the fraud for

one period, their greed may lead them to try it repeatedly. Therefore, a multi-period fraud

is likely.

3.4 The real peer company rN= ET SALES (source account) and its phony (f)

partner2

The common criminal would make a fraudulent entry, where the debit and the cred= it do

balance. Accounting software may prevent unbalanced entries. Such entries may also b= e

too obvious and too easy to detect. Therefore, to supplement the present fraudu= lent entry,

the criminal should make a balancing entry in other accounts that normally bala= nce the

source account. Such an account could be the (focus company) fRECEIVABLES. Such

an account normally couples the source account, in this environment. Furthermo= re, it is

not as easily verifiable as the cash account, for example. Since it is much eas= ier to verify

the balance of the cash account and to detect phony balances, the criminal may prefer to

use accounts other than cash, for the phony entries. Likewise, auditors are usu= ally less

likely to audit it as compared to the cash account. The peers (fraud free) less the focus

company balance is the fraud.

RAT will help us pinpoint the source of the fraud and estimate its damages. Thi= s

should help us detect it, if we can possibly classify it correctly (variable versus fixed).

For example, if the fraud positively correlates with sales, or varies proportionately with

the sales account, that gives us a clue as to which account actually contains t= his VF. Such

a fraud most likely originates from sales itself, or one of its derivatives, = such as sales

commissions. In contrast, we can more easily eliminate fraud sources that typically do

not vary with sales, such as fixed assets and depreciation frauds of all kinds.=

3.5 Activity base cost/fraud (ABC/F), short and long runs and regression

analysis

The developments in ABC since the late 1980s have improved accounting informati= on

systems. However, no one has ever applied ABC to fraud, which is the focus of the

current study. Applying ABC to fraud produces ABF. The literature covers other=

extensions of ABC to management, or activity based management (ABM), but not to

ABF. Hartnett and Lowry (1994) predict total cost for change in product mix. We = are

also trying to predict costs, except that we predict the cost of fraud damages a= s a function

of fraud mix (VF, FF and MF). Holmen (1995) suggests that ABC has primarily a<= o:p>

long-run horizon. Therefore, we apply it to long-run fraud estimation problems, frau= ds

that continue for one year and usually much longer. Macintosh (1994) suggests th= at in the

‘scientific ABC’ method, the designer uses multivariate regression analysis.=

Hartnett et al. (1994) confirm that ABC supplies better approximations of long-run

variable costs. This is one of the reasons that we are focusing strictly on long-term frauds.

Likewise, we suspect that ABF may also lead to similar results; it may supply better<= o:p>

results in the long run, rather than short run analysis. Even if we detect no mater= ial fraud,

just by estimating the fraud, we may reduce its damages by acting as a deterrent. There

are, however, obstacles to using ABF, much like ABC in reporting. Such obstacles=

include applying standards rather than actual values, subjectivity and verifiabilit= y in the

choice of cost-drivers and ABC or ABF integration into the nominal ledger. We use = the

pre-fraud as surrogates of fraud free standards, normally based on competitors’= financial

Uncooking the books from tox= ic paper sub-prime mortgages 11=

statements. The problem is that selecting proper competitors is subjective and may vary=

greatly depending on the industry, economy and other variables.
Pattison and Arendt (1994) implemented a modified ABC system. We have modified<= /o:p>

ABC so much that we have to rename it ABF. It is too different from ABC, to ret= ain the

same name. Sheu and Wacker (1994) integrate time series forecasting and ABC analysis.

Similarly, we are doing the same thing. We also integrate time series into ABF. Datar = and

Gupta (1994) suggest that an ABC generates more accurate product costs than other=

systems. For a similar reason, we hope that ABF will generate more accurate fraud co= st

estimates than other systems. Groth and Kinney (1994) suggest ABC and cost driver

analysis may reduce business risk, promoting value creation in a firm. Similarly, we=

contend that ABF and its cost drivers may help reduce fraud risk. In a counter sens= e,

fraud cost management not implemented properly result in an intensified eradicati= on of

value. These reasons attest to the importance of studying fraud costs in value creation.

This may ultimately lead to a cost benefit analysis of fraud eradication investm= ent.

3.6 ABF as a control for LT fraud and as a function of sales

Mak and Roush (1994) argue that ABC controls costs. Likewise, we feel that ABF<= o:p>

controls. Under one proposal, they formulate flexible budgets for each activity using= the

cost driver for that activity. We are using sales or some of the financial ratio= s as the

surrogate cost drivers of the fraud in order to develop an estimation method. In the future,

we may try other cost drivers. Smith (1994) extends ABC to a management context and

calls it: ABM. Similarly, we extend ABC to ABF. Our extension is a bit less radic= al in

the sense that we can view fraud as a cost category, while management is a much broader

concept than cost alone. We all agree that most of the emphasis in the literature, = thus far,

has been on costs (ABC) and quality (total quality management). We also consider other

areas and factors. A time-based focus has a number of positive implications for t= he

management accountant in designing improved management information systems. This<= /o:p>

ensures that decision-making is linked to an appropriate time horizon by matching s= hort

run and long run costs with decisions. For these reasons, we focus on the long = run in the

current model, reserving the short-run for future studies.

3.7 Literature of fraud, damages, detection ES and disclosure

A major problem in fraud detection is the lack of education on the part of th= ose who must

detect it (Kerwin, 1995). Some early warning signs may be found by parsing financi= al

statements (Donlan, 1994). We combine an SF educational approach with parsing

financial statements to find the best fraud predictor as warning signs in our ABF the= ory.

Redirected cash flows, the wrong receivables or the wrong disbursement location is a

common early warning sign of a cash fraud (Marks; Arnette, 1994). Employees commit=

the overwhelming majority (90.8%). Although at a much lower rate, executive fra= ud is

only 26% (Campbell and Lindsay, 1994). Therefore, SF deals with an employee frau= d,

such as a cash fraud. The Financial Fraud Detection and Disclosure Act, requires=

exception reporting when control systems fail, such as material financial frauds=

(Campbell and Lindsay, 1994). Neural networks can help to find patterns and

relationships, even obscure and nonlinear relationships (Stewart, 1994; Basch, 1994;<= /o:p>

Mayor, 1994). In our SF the relationships are fairly linear; therefore, we use lin= ear

regression. One way to combat management fraud involves analytical procedures (AP).

Comment [t3]: <= /span>Author: Please

confirm the year of publication (whether

1994 or 1995). Reference entry: Smith,

M. (1995) ‘Bottleneck management’,

Management Accounting= , Vol. 73, No. 3,

pp.26–28.

Comment [t4]: <= /span>Author: Please

provide full reference or delete from the

text if not required.

Comment [t5]: <= /span>Author: Please

provide full reference or delete from the

text if not required.

Comment [t6]: <= /span>Author: Please

provide full reference or delete from the

text if not required.

Comment [t7]: <= /span>Author: Please

provide full reference or delete from the

text if not required.

Comment [t8]: <= /span>Author: Please

provide full reference or delete from the

text if not required.

Comment [t9]: <= /span>Author: Please

provide full reference or delete from the

text if not required.

Comment [t10]: = Author: Please

provide full reference or delete from the

text if not required.

Comment [t11]: = Author: Please

provide full reference or delete from the

text if not required.

12 A. Rushinek and S. Rushinek<= o:p>

Quantitative APs alone will not detect fraud; they simply signal the likelihood of a

problem (Calderon and Green, 1994). This is where our model comes in. Our model

actually estimates the dollar damage of the fraud, after the AP signalled a high likelihood

of a problem (Rushinek and Rushinek, 1997, 2001).

Comment [t12]: = Author: Please

provide full reference or delete from the

text if not required.

4 Overview and definition of problems

The problem is that we do not know how to eradicate and prevent fraud. In addit= ion, we

do not know even how to estimate fraud and how to trace its sources, which may= be a

prerequisite to prevention. Estimating fraud and tracing it back to its initial transact= ion is

the focus of this study. To find the initial fraudulent transaction we developed the RAT.

RAT states that, unlike ordinary accounting, where we start from individual transactions

and conclude with summarised financial reports, the reversal may be more effect= ive for

fraud estimation and definition. Thus, RAT starts from the summarised financial reports,

which may be conceal fraudulent transactions, concluding with estimating the valu= e of

the fraud and defining the fraudulent transaction.

To define the culprit transaction, we use the %DFR, such as the dollar sales p= er

employee or quick ratio, instead of the original accounts, such as cash or inventory. There

are some advantages to using %DFR instead of using the original accounts. There= are

fewer %DFRs (dozens) compared to the original accounts (hundreds). The process of=

eliminating the irrelevant suspects is easier. The %DFR are much more standardised and<= o:p>

uniform across times-series, companies and industries, than the original accounts

themselves. The relative (percentage) unit of measurement is more comparable than the

original unit of measurement (inter or intra company or industry or currency compari= sons

of $ sales per employee to the quick ratio)

Our accuracy of spotting and forecasting the existence of frauds is ‘extremely good’

(Fanning et al., 1995; Coates and Fant, 1991). Likewise, we know what the red flags = that

indicate perpetrated frauds are (see Pincus, 1987). This sounds sufficient to scare auditors

about being held liable for not reporting the fraud (AICPA, 1988). Yet, such indications

are much too vague to enable auditors, prosecutors, or managers to pinpoint the fraud.

We still do not know how to estimate that fraud, what activity generates such frauds and

which transaction initiates such fraud. We think that if we can figure out the activity that

generates the fraud, we should be closer to tracking the fraudulent transaction and

eventually the culprits themselves. For that we propose an ABF theory.

We view fraud as a cost item. Fraud is certainly not a revenue item, nor is it= a

liability, assets or an equity item. Therefore, it is most similar to cost items, even= in the

sense that we are undoubtedly trying to minimise fraud (at least in theory). In f= act, fraud

may be the oldest cost, from biblical times, yet we do not estimate and account= for it like

any other cost. Fraud is currently the leading cause and cost of auditing litigation

(Palmrose, 1991; St Pierre and Anderson, 1994). Auditors have changed the way they

operate due to the increase of litigation (O’Malley, 1993; Elgin, 1992; Fuerm= an, 1992).

Auditors charge escalating fees to fund such litigation risk.

Based on ABC theories, every cost originates from some activity. Such activity is= the

cost driver, which are among the best cost predictors. Therefore, if we can treat fraud like

any other cost, we may apply ABC to fraud. Like any other cost, certain activit= ies

produce fraud. Therefore, it only follows that like any other cost, every fraud may have

Uncooking the books from tox= ic paper sub-prime mortgages 13=

its own drivers, which are its best predictors. We try to develop a method of identifying

these drivers for specific types of fraud, using ABC methods.
Unlike any other costs that are legal, fraud is illegal. Therefore, we cannot simp= ly use

traditional transaction-to-financial reports accounting. Thus, we deploy the RAT

approach and combine it with ABC. From this, we create the activity base fraud (ABF)=

theory. ABF states that if we can find the BP%DFR, we will be further along the way= of

estimating the fraud, its drivers and eventually it original culprits. Furthermore, by just

deploying ABF, it may act as a deterrent to fraud. Just as we are using standards to = cost

the unknown overheads, we may eventually also use standards to cost the unknown=

frauds.

We have some red flags that fire up whenever the likelihood of multi-year fraud rises.

Others have clearly defined such flags (discussed above), so that not only an audi= tor

could use it, but also a computer base ES could use it, while it is emulating an auditor.

Our current objective is one step further, to estimate the amount of the fraud = and its

original transaction or activity that created that transaction.

For this purpose, we have simulated multiple frauds (SF) on the financial statements

of this company. The SFs include increasing net income over the last few years. Thus for

example, we could have overstated sales and understated all the expenses by 10% of t= heir

values. At the same time we have also entered a balancing entry, so that the debit = and the

credit remain balanced and do not obviate the frauds.

We have then benchmarked the SF company against its own financial ratios prior= to

the SF. We use that to emulate how a real fraud ridden company may compare to i= ts

peers, which are not involved in the same type of frauds. This way we hope to iden= tify a

methodology of spotting red flags and patterns to detect and deter fraud.

4.1 Overview of banking indu= stry

At the nation’s 11,970 FDIC-insured institutions, total assets grew $84 billion from

September 30 to December 31, compared to growth of $90 billion during the fourth=

quarter of 1994. Fourth-quarter asset growth was funded to a greater extent by depo= sits in

1995 than in 1994. Total deposits increased $96 billion during the fourth quarte= r of 1995,

with most of this growth occurring in domestic deposits at commercial banks, whi= ch were

up $91 billion. In 1994’s fourth quarter, by comparison, total deposits increased $70

billion, including just $42 billion in domestic deposit growth (Helfer, 1995).<= /o:p>

The reserve ratio of the Bank Insurance Fund (BIF) was 1.30% of insured deposit= s on

December 31, down nominally from 1.31% on September 30 but still above the statutory=

minimum of 1.25%. The reserve ratio of the Savings Association Insurance Fund (SAIF= )

rose from 0.43% to 0.47% during the fourth quarter but remains far below the tar= get level

of 1.25%. As a result of the BIF’s full capitalisation, the FDIC was abl= e to reduce BIF

assessment rates twice in the latter half of 1995. The average BIF assessment rate fel= l

from 23.3 cents per $100 of assessable deposits to just 0.4 cents (effective Jan= uary 1,

1996), improving the attractiveness of BIF-assessable deposits relative to other funding

alternatives. Because SAIF assessment rates cannot be lowered significantly until the

fund is fully capitalised, the average assessment ra= te for SAIF members remains at

23.7 cents per $100.

14 A. Rushinek and S. Rushinek<= o:p>

1 This premium disparity between the BIF and the SAIF may partially explain deposit

growth patterns in 1995. Deposits assessable by the BIF grew $110 billion (4.6%)

during 1995, with $83 billion of the increase coming in the fourth quarter. Deposi= ts

assessable by the SAIF grew $20 billion (2.8%) in 1995 but decreased, by less than

$1 billion, in the fourth quarter.

2 Other factors, such as loan demand, also may affect deposit growth patterns=

(Helfer, 1995).

4.2 Barclays Bank company profile

Group profit before tax improved by 1,198 m as a result of a decline in provisions for bad

and doubtful debts. Of this reduction, 856 m occurred in the UK, where new gros= s

specific provisions were 791 m and 309 m in the USA, where new gross specific

provisions were 97m. Although falling significantly, non-performing lending remained a= t

a high level, particularly in regard to the UK property, construction, hotel = and leisure

sectors. As a consequence of work that is being undertaken to improve the assessment= of

credit and credit losses throughout the business, the general allowance to cover

unidentified bad debts has been increased by 74 m to 850 m (Barclays, 1994b).=

Profit before tax showed a significant improvement over the two previous years as<= o:p>

budget debt provisions fell, helped by releases in the UK and the rest of Europe. = The

reduction in provisions was achieved despite one large corporate provision of 65 m in=

Europe and further provisions in Canada (Barclays, 1994b).

4.3 Future of Barclays Bank Company

The trust company profit for the year was adversely affected by an ongoing=

reorganisation process, the result of which will be to improve efficiency and customer

service in the future. Most of the group’s trading activities are customer oriented. In

anticipation of future customer demand, the group maintains access to market liquidity b= y

quoting bid and offer prices with other market makers and carries an inventory of capital

market instruments including a variety of derivative and non-derivative (or ‘cash’)

financial instruments. Positions are also taken in the interest rate, foreign exchang= e, debt,

equity and commodity markets based on expectations of future market conditions. Th= ese

activities constitute the proprietary trading business of the group. Given the relationships

between instruments and markets, trading strategies depend on both market-making an= d

proprietary positions and are managed in concern in order to maximise trading related

revenue. Trading positions and any offsetting hedges are established as appropriate = to

accommodate customer or group requirements (Barclays, 1994a).

Uncooking the books from tox= ic paper sub-prime mortgages 15=

5 Methods and procedures, data analysis and interpretation

5.1 Data definition, input a= nd output of regression analysis theories

5.1.1 Differenced financial ratios (DFR) measure fraud impact and identify

fraud insensitive ratios

We download the company profile, annual report and financial ratios from the disclosure

database and then upload them into our fraud simulator spreadsheet software programme

(the programme). The programme<= /span> recalculates all these financial ratios verifying their
values and extending their decimal point. Then the programme<= /span> applies the SF to the

recalculated and verified annual report, producing a second set of phony fraudulent=

fAnnual reports. The programme inputs the fAnnual reports to the financial ratio

calculation and produces a second set of phony fraudulent financial ratios. The differe= nce

between the 1st real financial ratios and the second fraudulent financial ratio produces the

DFR. Such DFR measures the effects of a fraud on that particular ratio. If the D= FR equals

zero, then we can conclude that a particular fraud does not affect it. Likewise, = if the DFR

of another ratio also equals zero, then we can conclude that like the 1st rati= o, the same

fraud does not affect the second ratio. Thus, both ratios are insensitive to this fraud. The

DFR is only comparable when it is zero. When it is different than zero, we cann= ot use it

effectively for comparisons.

5.1.2 Fraud damage cost estimating software programme (FDCESP) and=

DFR/%DFR two stages

Due to the difficulty that computers have in calculating divisions by zero, it = is sometimes

necessary to initially use a measure like DFR. In developing an FDCESP we have to be<= o:p>

considering such issues. Since we want to extended current ES technology with such an

FDCESP system, we have to consider machine difficulties. Furthermore, since we

typically want to minimise computer resources and maximise efficiency, we would use

DFR as well as %DFR. We have to calculate DFR before we can calculate %DFR.

Therefore, we will also use it in our FDCESP development as a two stage process. The

programme will first calculate and use DFR, if it can choose the most sensitive ratio= . If it

can decide at this stage, it can then store its decisions in a case based reaso= ning

knowledge base (CBEKB). The programme needs to go no furt= her. It does not need to

calculate %DFR. This may be a case where the DFR will be zero, anyway. Therefore, we<= o:p>

do not need to calculate %DFR for a decision. Furthermore, the attempt to calculate it

may result in a division by zero error, creating all kinds of problems.

5.1.3 Case based reasoning knowledge base (CBEKB) and ES

In this context, a CBEKB is a data base of fraud cases and its related decisio= ns. Such

CBEKB can be a part of an ES that issues fraud detection, damage estimation,=

investigation and prevention opinions (Rushinek and Rushinek, 1993, 2007, 2008). Such

opinions will rely on a cost benefit analysis comparing the benefits of fraud damage=

reduction to its costs. This is where FDCESP integration fits. For a system like this= to

work, identifying a few best predictors of fraud value could be very useful. Thes= e

variables will be the BP%DFR.

16 A. Rushinek and S. Rushinek<= o:p>

5.1.4 BP%DFR flags fraud patterns

BP%DFR variables will estimate the dollar (or other currency) value of damages. Su= ch

analysis should be done when existing ES predict that the financial statements do contain

fraud, and the main question is which kind of fraud is it? Is it a credit sales and fixed

assets fraud? Is it a credit sales and accounts receivable fraud? This is where various

BP%DFR can red flag the various types of frauds that may be present. As we can

reasonably expect, different ratios will become BP%DFR for different types of fraud. F= or

example, if the fraud pattern appears to have fixed values over time, then it may be= a FF.

It is more likely to originate in fixed assets accounts rather than in a sales commission

account. In contrast to FF, a VF is more likely to originate in a sales commission account

or may be in a direct labour cost type of account. Likewise, an MF may be split over both

types of accounts, etc. Furthermore, this BP%DFR will estimate the total dollar amount

of the fraud telling us how many resources will it justify and most importantly which

accounts are the culprits.

5.1.5 Regressing fraud value= s on BP%DFR

Regressing fraud values on BP%DFR will help build a model to estimating fraud values.<= o:p>

To set up the data for such a regression analysis, we split the data into two parts. The first

part constitutes the pre-fraud condition. Naturally, for these observations, the independent

predictor variables, the BP%DFR, as well as the dependent predicted variable, the fra= ud

values, are both zeros. In an experimental simulation, that is certainly true. This= is saying

that if no fraud what so ever exists, then the pre-fraud and the post-fraud rati= os will be the

same. Thus, the BP%DFR, as well as all other differenced ratios, must equal zero.= In

contrast, in the real world, if no fraud what so ever exists, then the peer review company,

or companies and industry averages (like pre-fraud, free fraud) and the focus company

(like the post-fraud, fraud ridden) ratios will not necessarily be the same. Howe= ver, they

may approximate identity, if the peers are almost identical to the focus compan= y.

Although, identity between a focus company and its peers is very unrealistic conditio= n,

there are ways to approximate such condition, by statistical and mathematical mea= ns.

Such methods are beyond the scope of the present study, however, we will discuss them

briefly later and in future studies. The second part of these observations will typically be

different than zero. These two parts will make up two parallel time series, first is = time

series without fraud and the second one with fraud. Using these two sets of time series in

parallel, makes this analysis a cross sectional and time series regression analysis.<= o:p>

5.2 Data input for the regression analysis: its empirical aspects

5.2.1 Net sales/total assets= top Barclays Bank PLC predictor ratio3

For the top predictor, X1 BP%DFR, X1 =3D net sales/total assets, we compute the= 2nd part

of the positive fraud observations, as follows. Following are steps that calcu= late some of

the predictor variables’ values, such as X1, X2, etc. We start with the f= irst variable, X1.

The fraudulent, phony, fNET SALES/TOTAL ASSETS has a value of 0.13049. In<= /o:p>

contrast, the real, rNET SALES/TOTAL ASSETS, has a value of 0.10669. To difference,

one needs to deduct the fraudulent from the real balance. The difference can be= the gain

in NET SALES/TOTAL ASSETS of 22.307620%. This percentage loss (–) or gain (+) in

the ratio due to the fraud is the value of X1, the 1ST BP%DFR independent predi= ctor

Uncooking the books from tox= ic paper sub-prime mortgages 17=

variable. Applying the same process to SG&A/SALES, and NET SALES/EMPLOYEES,<= /o:p>

you calculate X2 =3D SG&A/SALES and X3 =3D NET SALES/EMPLOYEES, the additio= nal

BP%DFRs. Calculate the dependent predicted variable, Y =3D NET SALES, as the MF,

sum of FF and VF and you are ready to regress it on the BP%DFRs.

The MFCF is calculated by regressing VF, FF and MF on net sales. Start with VF.=

We classify such a VFCF as a case of pure VC definition. Continue with FF. The= FF

regression has an intercept of $1,318,800.00 and a Slope of 0.00%. We classify such a<= o:p>

FFCF as a pure FF definition. Proceed with MF. The MF regression has an RS of 1,= an

intercept of $1,318,800.00 and a slope of 10.00%. We classify this MFCF as a pure MF<= o:p>

definition. Now, instead of regressing the sum, MF =3D VF + FF, try adding the intercep= ts

and slopes of the separate VF and FF regression, producing a 2nd redundant, but=

independently calculated intercept and slope. Deduct the independently calculated

intercepts and slope.

Theoretically, for a pure VF definition, the intercept should be a perfect zero, but that<= o:p>

may change due to rounding errors. A gross VF definition may cause an error, if= we

define the VF as a percentage of a primary account (not perfectly correlated with = net

sales). Such an account may be LT assets. The FF should be the exact complement

(mirror image) of the VF, a slope of zero and an intercept that is positive. At the same

time, the VF should have the opposite values, zero intercept and a positive slope. Their

sum, MF, should have both positive intercept and slope. Likewise, we made MFCF b= y

combining the separately regressed VF and FF. These two functions (VF and FF) should<= o:p>

have the same two regression parameters (intercept and slope). Much like, the si= ngle

MFCS regression, it should also have an intercept and slope. These redundant calculation

controls demonstrate how to promote software integrity. Classify and decomposing fra= ud

into its parts will help detect, measure and ultimately minimise fraud damages.<= o:p>

The RS shows the statistical quality of this model, which we will discuss later. Such

MFCF can estimate fraud prevention benefits. It traps error by comparing actual = and

expected results. Thus, the growth rate and variance of FF should be zero. If it ind= eed is

zero, then we may have avoided this error. In this case, these error values are e= qual to

0.00%. Even if it is not zero, it may be due to an immaterial error such as a roun= ding

error, or an error in the database. This is beyond the scope of this study.= 4

ES exemplify regressions’ rules. The ES can regress the estimated fraud values

(estimated by our model) on the NET SALES account of the focus company. If the

regression is significant, then the ES may reach some tentative conclusions, about the=

possibility of fraud. This can be true, if it turns out to be a case of a pure definiti= on of VF,

FF and MF. The ES further reinforces its conclusion that fraud may exist, estimating its

CF structure, dollar estimates and probabilities. More analysis needs to be us= ed to further

reinforce such suspicions and eventually come up with an explanation to the fraud. ES=

show that explanations to the VF can include: 10% inflated net sales and receivables, to

increase gross profit and raise stock market values. Our methods may help ES calcula= te

risks and correctly interpret them. Are the ratios correct? At this point one can= not be sure

if there is a fraud or not. This ES tests itself, using a value and sign test = as internal

controls of integrity and reliability.5<= /o:p>

18 A. Rushinek and S. Rushinek<= o:p>

6 Summary output for Barclays = Bank PLC6

The regression statistics sub-analysis contains the three parts. The 1st part is the multiple

R, 0.999845. The second part is the lower RS, R**2, value of 0.99969. The third part,

following the RS is the even lower adjusted R-square (ARS) of 0.999632. Multiple R is=

the correlation between the predicted and the actual values of the focus compan= y,

Barclays Bank PLC, the coefficient of correlation. These correlate the actual and th= e

forecasted values of this SF.

6.1 Bank ES rules= 7

This regression analysis helps ES form patterns of rules for fraud classificatio= n. The ES

will apply such rules to a suspicious bank, to classify its fraud. We will form = the ES rule

in a step by step fashion as we discuss the output 0.999632. Following is the = 1st

component of this rule, dealing with RS:

For the regression of the differenced account on the DFR:

If the Industry is equal to This Industry and the Suspicious Company R-Square = is Equal

To

Less than the upper bound of R**2: 0.99969, And

Greater Than the Lower bound of ARS: 0.999632 , And

In practice this will be one large rule, for simplicity we break it into its components.

Arrow heads indicate that the rule continues. A banking knowledge base system

(KBS) will store these rules.

6.2 Multiple R (R) – internal software control, the sum of square (SS) and beta

values=

If the multiple R is equal to one, then a change in the value of X, the independent

predictor variable, the BP%DFR predicts the dependent predicted variable, the focus

company, Barclays Bank PLC BP%DFR, perfectly well. Otherwise, multiple R is

different from one, and the prediction is less than perfect. The present multiple R o= f

0.999845 tells us that there is relatively strong linear relationship. The independe= nt

variables (the BP%DFR of Barclays) and the COB industry estimate the SF very well.

This means that if the BP%DFR increases, then the SF is also very likely to incr= ease as

well, in this sample of data. If this is statistically significant, it may apply = to other

samples.

6.3 RS: variance in the SF explained by the financial ratios

Unfortunately, we cannot interpret R in more precise terms, since it is not equal to one.<= o:p>

The RS, the coefficient of determination, may help us determine the meaning of = the

relationship of the population more precisely. ES would typically apply such relationshi= p

to the population of cases.

The multiple R (R), 0.999845 squared is equal to, RS, 0.99969. The RS should be=

lower than R, because we square a fraction. If RS is equal or larger than R, we m= ost

likely have an error. The RS is the coefficient of determination. The RS shows how= the

Uncooking the books from tox= ic paper sub-prime mortgages 19=

independent variable determines the dependent variable. The independent (predictor)

variables are some ratios, the BP%DFR. In contrast, the SF is the dependent (predicte= d)

variable. R shows that BP%DFR predicts the SF (in the focus company) very well, close=

enough to 100%. This way ES estimate fraud damage as a function of such BP%DFR, fo= r

a cost benefit analysis.

6.4 The ARS: a function of t= he R and the number of observations

The ARS, 0.999632 is lower than both the multiple R and the RS.8 We adjust the ARS for

the degrees of freedom (df). The number of observat= ions, 20, determines the df. This
ARS tempers the strength of the R, which may be unrealistically high, especiall= y if we

have a small number of observations. To avoid overstating the strength of RS, we=

calculate the ARS. This difference between the RS and ARS will shrink as we increase<= o:p>

the number of observations. We split these observations into two parts. The 1st half (ten

observations) includes zero values of both the predictor financial ratios, BP%DFR and

the predicted SFs. These zero values represent no difference in financial ratio= s in the

absence of fraud. Thus, the difference between the fraudulent and the real financial ratios,

as well as the SF itself is all equal to zero. The = second half shows the differences in the

financial ratios that explain the SF. In contrast to the origin values of zero, these later ten

observations will usually differ from zero. Otherwise, we may not have simulated a<= /o:p>

fraud; we know this by definition and design.

7 ANOVA: testing the utility of the model

The difference between the RS and ARS will shrink as we increase the number of<= o:p>

observations. If such differences exist, then, the ANOVA portion will explain them.<= /o:p>

Specifically, the SSE [sum of squared (SS, column) error or residual (row)], that is much=

greater than zero, explain some such differences. In our case, the value of SS resi= dual is

1.1E + 10, which is very small and very close to zero, explaining the proximity = of RS and

ARS.

The ANOVA helps us evaluate the utility of the entire model. It tests individua= l

coefficients allowing us to conclude that a linear relationship between the independent<= o:p>

and the dependent variables exists. The ANOVA decomposes the variability of the=

dependent variable, The emulate fraud, measured by SS total, 3.55E + 13 into its=

components, the regression SS, 3.55E + 13 and the residual part, 1.1E + 10. If the SSR<= o:p>

(regression) is large relatively to the SSE (residual), the RS is high – signifyin= g a good fit

and a good model, as is our present case. A value of F, 17225.41, shows the significance

of the model. This F value is statistically significant at the 0.05 level. This means that the

BP%DFR, Xs independent variables, explain the variation in Y dependent variable, th= e

SF dollar amount for Barclays Bank PLC. Therefore, the model is useful for estimating

the cost for this kind of fraud damages.9

8 ANOVA predictors-test of the intercept constant and the beta slope(s)

The test of the intercept and/or individual beta coefficients allows us to deci= de about the

linearity assumption. We decide whether a linear relationship exists between the=

20 A. Rushinek and S. Rushinek<= o:p>

independent predictor financial ratios and their predicted dependent variable, the SF i= n

the focus company, Barclays Bank PLC. Consequently, we perform the t-test for t= he

intercept constant, –13.1994 and the predictor beta value for Barclays Bank PLC= is

–1E + 07. Our test criterion is the P-value.

To evaluate the statistical significance of the intercept coefficient, –13.1994, we

compare it to its standard error. The standard err column, 8,288.838, for the intercept,

along with the relatively small differences between the t-stat and the P-value columns,

–0.00159 and 0.998749, confirm our notion from the theory. We cannot reject the null=

hypothesis, since this P-value, of 0.998749, is greater or equal to 0.05. Thus, we cann= ot

reject the null hypothesis, that the intercept value is zero, concluding that it is zero, when

the value of the other variables is also zero. Namely, in the absence of any differences

among the financial ratios the model assumes no fraud. Therefore, the focus and t= he peer

companies (DFR equal zero), at the origin, the forecasted fraud (intercept) must also=

equal zero. However, if we are farther away from the origin, the intercept can be used

together with the other variables and then the entire model is statistically signifi= cant at the

0.05 level, as the ANOVA demonstrated. Thus, the origin and its vicinity are out= side our

relevant range. When we apply this model, we know that the fraud is greater than zer= o.

8.1 ES form patterns of rules for ANOVA and intercept’s upper and lower

bounds=

Using the ANOVA bounds of the Intercept, the ES can continue forming the rules as=

follows:

If The ANOVA is statistically significant at the 5% level, & The Suspiciou= s

Company’s Intercept is:

Less Than Intercept Coefficient Upper 95% Bound Of: 17558.35, And

Greater Than Intercept Coefficient Lower 95% Bound Of:-17584.7, And =3D=3D>=

Expert Systems (ES) Form Patterns Of Rules For Predictor (Independent) Variables: = The

X1-3 Coefficients Or Slopes, & Their 95% Upper And Lower Confidence Interval (CI)

Bounds

We will plug the X1-3 coefficients or slopes of the variables in the final regression

equation. To complete the regression equation we need the residual. Therefore, we wil= l

do that in the after the residual output analysis. In the meantime, we can complete the

fraud pattern ES rule, using the 95% upper and lower bounds of this variable toge= ther

with the RS and the intercept, as follows:

=3D> the 1st Best Predictor is: X1 =3D NET SALES/TOTAL ASSETS , And

its Coefficient (Slope) value is Greater Than 95% lower Bound of: -1.5E+07;

The 2nd Best Predictor is: X2 =3D SG & A/SALES (same as above but for X2 Bo= unds), And

The 3rd Best Predictor is X3 =3D NET SALES/EMPLOYEES

Then it is most likely due to a: 10% inflated net sales and receivables to incre= ase gross

profit and stock market value type of the perpetrated fraud.

At this point, the ES will also fire an explanation as to support its decision (Rushinek

and Rushinek, 2001).10 To complete the calculation of the estimated fraud, we need to

include the residual value, in addition to the values of the financial ratios. Thus= , we will

Uncooking the books from tox= ic paper sub-prime mortgages 21=

replace the X symbols by their values, and add the residual to the sum, that should=

produce the SF in a given year. In contrast to the insignificant intercept, the slo= pes are all

significant. Thus, we reject the null hypothesis that the slopes are all equal to zero. = We

can plug our parameters into the model after we figure out the residual value.<= o:p>

9 Conclusions

This study develops a ‘finger print’ definition for a specific class= of frauds, with a

specific rate, for the banking industry. The regression parameters define parts of t= he fraud

finger print. Some of these parameters include such variables as the upper and low= er

2005 Annual Meeting

Augu= st 7–10, 2005 San Francisco, California

Come to the City by the Bay!

CPE Session 2: Saturday, August 6, 8:00 AM – 4:00 PM

Sarb= anes-Oxley Microsoft® Accelerator Forensic Accounting, Expert Witness Testimony, and Computer Litigation Support

CPE Session 3: Saturday, August 4, 8:00 AM – 11:00 AM

Audited Google Video Professors' e-Store Pod-Casts: How to Teach, Servi= ce & Research using WWW Assessment DVD of Microsoft® Office Accounting Technologies for the Wireless Classroom

(Accounting)

CPE Session 8: Saturday, August 4, 1:00 PM – 4:00 PM

Assessment CPE Video Podcasts and Blogs "Hands-on:" Consult, Publish & Teach Forensic Auditing, Expert Witness Testimony & Microsoft® Office Accounting Computer Litigation Support

(Specialized Knowledge & Applications)

Description/Objectives:

Presenters:

Avi Rushinek, University of Miami

Sara Rushinek, University of Miami

Sponsor:

Teaching and Curriculum Section.

-----------------------------------------------------------------------= ---------

Note: CPE fields of study are in parentheses

CPE Session 8: Saturday, August 4, 1:00 PM – 4:00 PM

Assessment CPE Video Podcasts and Blogs "Hands-on:" Consult, Publish & Teach Forensic Auditing, Expert Witness Testimony & Microsoft® Office Accounting Computer Litigation Support

(Specialized Knowledge & Applications)

CPE Session 2: Saturday, August 2, 8:00 AM – 11:00 AM

Mock Trial & Forensic Faculty WWW Marketplace, Jury, Judge & Attorneys — Courtroom & Classr= oom Instructional Technology Research & Service

(Accounting - Basic)

CPE Session 11: Saturday, August 2, 1:00 PM – 4:00 PM

Role Play Forensic Accounting, Auditing & Tax Expert Witness: Providing Testimony and Computer Litigation Support Teaching, Services, Research and Development

(Accounting - Basic)

Augu= st 7–10, 2005
San Francisco, California

Sarb= anes-Oxley Microsoft® Accelerator Forensic Accounting,
Expert Witness Testimony, and Computer Litigation Support

Mock Trial & Forensic Faculty
WWW Marketplace, Jury, Judge & Attorneys — Courtroom & Classr= oom Instructional Technology Research & Service

Role Play Forensic Accounting, Auditing & Tax Expert Witness:
Providing Testimony and Computer Litigation Support
Teaching, Services, Research and Development